Perceptron::predictProbability() - Code Metrics - php-ai/php-ml - Measure and Improve Code Quality continuously with Scrutinizer

Perceptron::predictProbability() A
last analyzed 2020-05-15 05:48 UTC

↳ Parent: Perceptron

Complexity

Conditions	2
Paths	2

Size

Total Lines	11
Code Lines	5

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
eloc	5
dl	0
loc	11
rs	10
c	0
b	0
f	0
cc	2
nc	2
nop	2

<?php

declare(strict_types=1);

namespace Phpml\Classification\Linear;

use Closure;
use Phpml\Classification\Classifier;
use Phpml\Exception\InvalidArgumentException;
use Phpml\Helper\OneVsRest;
use Phpml\Helper\Optimizer\GD;
use Phpml\Helper\Optimizer\Optimizer;
use Phpml\Helper\Optimizer\StochasticGD;
use Phpml\Helper\Predictable;
use Phpml\IncrementalEstimator;
use Phpml\Preprocessing\Normalizer;

class Perceptron implements Classifier, IncrementalEstimator
{
    use Predictable;
    use OneVsRest;

    /**
     * @var Optimizer|GD|StochasticGD|null
     */
    protected $optimizer;

    /**
     * @var array
     */
    protected $labels = [];

    /**
     * @var int
     */
    protected $featureCount = 0;

    /**
     * @var array
     */
    protected $weights = [];

    /**
     * @var float
     */
    protected $learningRate;

    /**
     * @var int
     */
    protected $maxIterations;

    /**
     * @var Normalizer
     */
    protected $normalizer;

    /**
     * @var bool
     */
    protected $enableEarlyStop = true;

    /**
     * Initalize a perceptron classifier with given learning rate and maximum
     * number of iterations used while training the perceptron
     *
     * @param float $learningRate  Value between 0.0(exclusive) and 1.0(inclusive)
     * @param int   $maxIterations Must be at least 1
     *
     * @throws InvalidArgumentException
     */
    public function __construct(float $learningRate = 0.001, int $maxIterations = 1000, bool $normalizeInputs = true)
    {
        if ($learningRate <= 0.0 || $learningRate > 1.0) {
            throw new InvalidArgumentException('Learning rate should be a float value between 0.0(exclusive) and 1.0(inclusive)');
        }

        if ($maxIterations <= 0) {
            throw new InvalidArgumentException('Maximum number of iterations must be an integer greater than 0');
        }

        if ($normalizeInputs) {
            $this->normalizer = new Normalizer(Normalizer::NORM_STD);
        }

        $this->learningRate = $learningRate;
        $this->maxIterations = $maxIterations;
    }

    public function partialTrain(array $samples, array $targets, array $labels = []): void
    {
        $this->trainByLabel($samples, $targets, $labels);
    }

    public function trainBinary(array $samples, array $targets, array $labels): void
    {
        if ($this->normalizer !== null) {
            $this->normalizer->transform($samples);
        }

        // Set all target values to either -1 or 1
        $this->labels = [
            1 => $labels[0],
            -1 => $labels[1],
        ];
        foreach ($targets as $key => $target) {
            $targets[$key] = (string) $target == (string) $this->labels[1] ? 1 : -1;
        }

        // Set samples and feature count vars
        $this->featureCount = count($samples[0]);

        $this->runTraining($samples, $targets);
    }

    /**
     * Normally enabling early stopping for the optimization procedure may
     * help saving processing time while in some cases it may result in
     * premature convergence.<br>
     *
     * If "false" is given, the optimization procedure will always be executed
     * for $maxIterations times
     *
     * @return $this
     */
    public function setEarlyStop(bool $enable = true)
    {
        $this->enableEarlyStop = $enable;

        return $this;
    }

    /**
     * Returns the cost values obtained during the training.
     */
    public function getCostValues(): array
    {
        return $this->costValues;
    }

    protected function resetBinary(): void
    {
        $this->labels = [];
        $this->optimizer = null;
        $this->featureCount = 0;
        $this->weights = [];
        $this->costValues = [];
    }

    /**
     * Trains the perceptron model with Stochastic Gradient Descent optimization
     * to get the correct set of weights
     */
    protected function runTraining(array $samples, array $targets): void
    {
        // The cost function is the sum of squares
        $callback = function ($weights, $sample, $target): array {
            $this->weights = $weights;

            $prediction = $this->outputClass($sample);
            $gradient = $prediction - $target;
            $error = $gradient ** 2;

            return [$error, $gradient];
        };

        $this->runGradientDescent($samples, $targets, $callback);
    }

    /**
     * Executes a Gradient Descent algorithm for
     * the given cost function
     */
    protected function runGradientDescent(array $samples, array $targets, Closure $gradientFunc, bool $isBatch = false): void
    {
        $class = $isBatch ? GD::class : StochasticGD::class;

        if ($this->optimizer === null) {
            $this->optimizer = (new $class($this->featureCount))
                ->setLearningRate($this->learningRate)
                ->setMaxIterations($this->maxIterations)
                ->setChangeThreshold(1e-6)
                ->setEarlyStop($this->enableEarlyStop);
        }

        $this->weights = $this->optimizer->runOptimization($samples, $targets, $gradientFunc);
        $this->costValues = $this->optimizer->getCostValues();
    }

    /**
     * Checks if the sample should be normalized and if so, returns the
     * normalized sample
     */
    protected function checkNormalizedSample(array $sample): array
    {
        if ($this->normalizer !== null) {
            $samples = [$sample];
            $this->normalizer->transform($samples);
            $sample = $samples[0];
        }

        return $sample;
    }

    /**
     * Calculates net output of the network as a float value for the given input
     *
     * @return int|float
     */
    protected function output(array $sample)
    {
        $sum = 0;
        foreach ($this->weights as $index => $w) {
            if ($index == 0) {
                $sum += $w;
            } else {
                $sum += $w * $sample[$index - 1];
            }
        }

        return $sum;
    }

    /**
     * Returns the class value (either -1 or 1) for the given input
     */
    protected function outputClass(array $sample): int
    {
        return $this->output($sample) > 0 ? 1 : -1;
    }

    /**
     * Returns the probability of the sample of belonging to the given label.
     *
     * The probability is simply taken as the distance of the sample
     * to the decision plane.
     *
     * @param mixed $label
     */
    protected function predictProbability(array $sample, $label): float
    {
        $predicted = $this->predictSampleBinary($sample);

        if ((string) $predicted == (string) $label) {
            $sample = $this->checkNormalizedSample($sample);

            return (float) abs($this->output($sample));
        }

        return 0.0;
    }

    /**
     * @return mixed
     */
    protected function predictSampleBinary(array $sample)
    {
        $sample = $this->checkNormalizedSample($sample);

        $predictedClass = $this->outputClass($sample);

        return $this->labels[$predictedClass];
    }
}


1			<?php
2
3			declare(strict_types=1);
4
5			namespace Phpml\Classification\Linear;
6
7			use Closure;
8			use Phpml\Classification\Classifier;
9			use Phpml\Exception\InvalidArgumentException;
10			use Phpml\Helper\OneVsRest;
11			use Phpml\Helper\Optimizer\GD;
12			use Phpml\Helper\Optimizer\Optimizer;
13			use Phpml\Helper\Optimizer\StochasticGD;
14			use Phpml\Helper\Predictable;
15			use Phpml\IncrementalEstimator;
16			use Phpml\Preprocessing\Normalizer;
17
18			class Perceptron implements Classifier, IncrementalEstimator
19			{
20			use Predictable;
21			use OneVsRest;
22
23			/**
24			* @var Optimizer\|GD\|StochasticGD\|null
25			*/
26			protected $optimizer;
27
28			/**
29			* @var array
30			*/
31			protected $labels = [];
32
33			/**
34			* @var int
35			*/
36			protected $featureCount = 0;
37
38			/**
39			* @var array
40			*/
41			protected $weights = [];
42
43			/**
44			* @var float
45			*/
46			protected $learningRate;
47
48			/**
49			* @var int
50			*/
51			protected $maxIterations;
52
53			/**
54			* @var Normalizer
55			*/
56			protected $normalizer;
57
58			/**
59			* @var bool
60			*/
61			protected $enableEarlyStop = true;
62
63			/**
64			* Initalize a perceptron classifier with given learning rate and maximum
65			* number of iterations used while training the perceptron
66			*
67			* @param float $learningRate Value between 0.0(exclusive) and 1.0(inclusive)
68			* @param int $maxIterations Must be at least 1
69			*
70			* @throws InvalidArgumentException
71			*/
72			public function __construct(float $learningRate = 0.001, int $maxIterations = 1000, bool $normalizeInputs = true)
73			{
74			if ($learningRate <= 0.0 \|\| $learningRate > 1.0) {
75			throw new InvalidArgumentException('Learning rate should be a float value between 0.0(exclusive) and 1.0(inclusive)');
76			}
77
78			if ($maxIterations <= 0) {
79			throw new InvalidArgumentException('Maximum number of iterations must be an integer greater than 0');
80			}
81
82			if ($normalizeInputs) {
83			$this->normalizer = new Normalizer(Normalizer::NORM_STD);
84			}
85
86			$this->learningRate = $learningRate;
87			$this->maxIterations = $maxIterations;
88			}
89
90			public function partialTrain(array $samples, array $targets, array $labels = []): void
91			{
92			$this->trainByLabel($samples, $targets, $labels);
93			}
94
95			public function trainBinary(array $samples, array $targets, array $labels): void
96			{
97			if ($this->normalizer !== null) {
98			$this->normalizer->transform($samples);
99			}
100
101			// Set all target values to either -1 or 1
102			$this->labels = [
103			1 => $labels[0],
104			-1 => $labels[1],
105			];
106			foreach ($targets as $key => $target) {
107			$targets[$key] = (string) $target == (string) $this->labels[1] ? 1 : -1;
108			}
109
110			// Set samples and feature count vars
111			$this->featureCount = count($samples[0]);
112
113			$this->runTraining($samples, $targets);
114			}
115
116			/**
117			* Normally enabling early stopping for the optimization procedure may
118			* help saving processing time while in some cases it may result in
119			* premature convergence.<br>
120			*
121			* If "false" is given, the optimization procedure will always be executed
122			* for $maxIterations times
123			*
124			* @return $this
125			*/
126			public function setEarlyStop(bool $enable = true)
127			{
128			$this->enableEarlyStop = $enable;
129
130			return $this;
131			}
132
133			/**
134			* Returns the cost values obtained during the training.
135			*/
136			public function getCostValues(): array
137			{
138			return $this->costValues;
139			}
140
141			protected function resetBinary(): void
142			{
143			$this->labels = [];
144			$this->optimizer = null;
145			$this->featureCount = 0;
146			$this->weights = [];
147			$this->costValues = [];
148			}
149
150			/**
151			* Trains the perceptron model with Stochastic Gradient Descent optimization
152			* to get the correct set of weights
153			*/
154			protected function runTraining(array $samples, array $targets): void
155			{
156			// The cost function is the sum of squares
157			$callback = function ($weights, $sample, $target): array {
158			$this->weights = $weights;
159
160			$prediction = $this->outputClass($sample);
161			$gradient = $prediction - $target;
162			$error = $gradient ** 2;
163
164			return [$error, $gradient];
165			};
166
167			$this->runGradientDescent($samples, $targets, $callback);
168			}
169
170			/**
171			* Executes a Gradient Descent algorithm for
172			* the given cost function
173			*/
174			protected function runGradientDescent(array $samples, array $targets, Closure $gradientFunc, bool $isBatch = false): void
175			{
176			$class = $isBatch ? GD::class : StochasticGD::class;
177
178			if ($this->optimizer === null) {
179			$this->optimizer = (new $class($this->featureCount))
180			->setLearningRate($this->learningRate)
181			->setMaxIterations($this->maxIterations)
182			->setChangeThreshold(1e-6)
183			->setEarlyStop($this->enableEarlyStop);
184			}
185
186			$this->weights = $this->optimizer->runOptimization($samples, $targets, $gradientFunc);
187			$this->costValues = $this->optimizer->getCostValues();
188			}
189
190			/**
191			* Checks if the sample should be normalized and if so, returns the
192			* normalized sample
193			*/
194			protected function checkNormalizedSample(array $sample): array
195			{
196			if ($this->normalizer !== null) {
197			$samples = [$sample];
198			$this->normalizer->transform($samples);
199			$sample = $samples[0];
200			}
201
202			return $sample;
203			}
204
205			/**
206			* Calculates net output of the network as a float value for the given input
207			*
208			* @return int\|float
209			*/
210			protected function output(array $sample)
211			{
212			$sum = 0;
213			foreach ($this->weights as $index => $w) {
214			if ($index == 0) {
215			$sum += $w;
216			} else {
217			$sum += $w * $sample[$index - 1];
218			}
219			}
220
221			return $sum;
222			}
223
224			/**
225			* Returns the class value (either -1 or 1) for the given input
226			*/
227			protected function outputClass(array $sample): int
228			{
229			return $this->output($sample) > 0 ? 1 : -1;
230			}
231
232			/**
233			* Returns the probability of the sample of belonging to the given label.
234			*
235			* The probability is simply taken as the distance of the sample
236			* to the decision plane.
237			*
238			* @param mixed $label
239			*/
240			protected function predictProbability(array $sample, $label): float
241			{
242			$predicted = $this->predictSampleBinary($sample);
243
244			if ((string) $predicted == (string) $label) {
245			$sample = $this->checkNormalizedSample($sample);
246
247			return (float) abs($this->output($sample));
248			}
249
250			return 0.0;
251			}
252
253			/**
254			* @return mixed
255			*/
256			protected function predictSampleBinary(array $sample)
257			{
258			$sample = $this->checkNormalizedSample($sample);
259
260			$predictedClass = $this->outputClass($sample);
261
262			return $this->labels[$predictedClass];
263			}
264			}
265

php-ai / php-ml

Perceptron::predictProbability() A last analyzed 2020-05-15 05:48 UTC

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Perceptron::predictProbability() A
last analyzed 2020-05-15 05:48 UTC